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| Presentation | 2022-03-02 15:35 [Poster Presentation] Interpolation of head-related transfer function from small amount of observation data using deep learning based on spherical wavefunction expansion Yuki Ito, Tomohiko Nakamura, Shoichi Koyama, Hiroshi Saruwatari (UTokyo)  EA2021-90 SIP2021-117 SP2021-75 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In binaural synthesis, listeners' individual head-related transfer functions (HRTFs) are necessary for highly-immersive spatial audio. Since HRTF measurement is generally time-consuming, it will be helpful if high-resolution HRTFs are interpolated from a small number of HRTFs obtained by a simple measurement procedure. One of the established HRTF interpolation methods is the method based on spherical wavefunction expansion, which allows estimating HRTFs at arbitrary direction and distance in a simple manner; however, its interpolation accuracy deteriorates as the number of measurements decreases. We propose a deep-neural-network (DNN)-based HRTF interpolation method combining the representation using spherical wavefunction expansion and meta-learning. Since meta-learning simulates the process of interpolation from a small number of measurements to learn DNN using training data, the proposed method will stably estimate HRTFs even when the number of measurements is insufficient. Experimental results indicated that the proposed method achieves high interpolation accuracy compared with the current method when the number of measurements is small. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | head-related transfer functions / HRTF interpolation / deep learning / meta-learning / few-shot learning / spherical wavefunction expansion / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 383, EA2021-90, pp. 163-170, March 2022. | |
| Paper # | EA2021-90 | |
| Date of Issue | 2022-02-22 (EA, SIP, SP) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | EA2021-90 SIP2021-117 SP2021-75 |
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| Conference Information | |
| Committee | EA SIP SP IPSJ-SLP |
| Conference Date | 2022-03-01 - 2022-03-02 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | EA |
| Conference Code | 2022-03-EA-SIP-SP-SLP |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Interpolation of head-related transfer function from small amount of observation data using deep learning based on spherical wavefunction expansion |
| Sub Title (in English) | |
| Keyword(1) | head-related transfer functions |
| Keyword(2) | HRTF interpolation |
| Keyword(3) | deep learning |
| Keyword(4) | meta-learning |
| Keyword(5) | few-shot learning |
| Keyword(6) | spherical wavefunction expansion |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Yuki Ito |
| 1st Author's Affiliation | The University of Tokyo (UTokyo) |
| 2nd Author's Name | Tomohiko Nakamura |
| 2nd Author's Affiliation | The University of Tokyo (UTokyo) |
| 3rd Author's Name | Shoichi Koyama |
| 3rd Author's Affiliation | The University of Tokyo (UTokyo) |
| 4th Author's Name | Hiroshi Saruwatari |
| 4th Author's Affiliation | The University of Tokyo (UTokyo) |
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| Speaker | Author-1 |
| Date Time | 2022-03-02 15:35:00 |
| Presentation Time | 120 minutes |
| Registration for | EA |
| Paper # | EA2021-90, SIP2021-117, SP2021-75 |
| Volume (vol) | vol.121 |
| Number (no) | no.383(EA), no.384(SIP), no.385(SP) |
| Page | pp.163-170 |
| #Pages | 8 |
| Date of Issue | 2022-02-22 (EA, SIP, SP) |
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